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Probability - The Science of Uncertainty and Data


MITx
Enrollment is Closed

About this course

Welcome to 6.3700, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example:

  • The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science".
  • A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions.
  • Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Netflix to the Office of Management and Budget.

The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.

Prerequisites

The prerequisite for 6.3700x is a year of college level calculus for those with undergraduate degrees from other universities.

Course staff

Professor Tsitsiklis

John N. Tsitsiklis is a Clarence J Lebel Professor of Electrical Engineering, with the Department of Electrical Engineering and Computer Science (EECS) at MIT. He is an associate director of the Laboratory for Information and Decision Systems (LIDS) and is also affiliated with the Operations Research Center (ORC).



  1. Course Number

    6.370x
  2. Classes Start

    2020-01-27T20:00
  3. Classes End

  4. Estimated Effort

    At lease 6 hours per week
  5. Requirements

    Elementary statistics will be useful